Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Oriolshhh/runware-image-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/oriolshhh/runware-image-mcp/image-art-direction)<a href="https://agentmods.dev/rules/oriolshhh/runware-image-mcp/image-art-direction"><img src="https://agentmods.dev/badge/rules/oriolshhh/runware-image-mcp/image-art-direction/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/oriolshhh/runware-image-mcp/image-art-direction"><img src="https://agentmods.dev/badge/rules/oriolshhh/runware-image-mcp/image-art-direction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.01207 |
| Opus 5 | $0.00010 | $0.00603 |
| Sonnet 5 | $0.00004 | $0.00241 |
| Haiku 4.5 | $0.00002 | $0.00121 |
Grade A, and why
image-art-direction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: image-art-direction
Image Art Direction
Purpose
Create a purposeful image system rather than a collection of unrelated decorations. Every proposed asset must improve a named page outcome and every generation prompt must preserve one approved visual language.
When to use it
- When an existing page feels visually flat, abstract, generic, or difficult to scan.
- When illustrations, editorial images, diagrams, product imagery, textures, or contextual scenes could improve explanation, trust, identity, or pacing.
- Before generating or integrating a family of visual assets.
Instructions
- Audit the rendered page and real content. Identify comprehension gaps, abstract claims, weak hierarchy, long text runs, trust gaps, empty states, onboarding moments, or transitions where an image has a clear job.
- Reject decorative filler. Do not add imagery where it competes with the primary action, repeats text without adding meaning, obscures product UI, increases cognitive load, or exists only to fill whitespace.
- Inventory existing brand assets, illustrations, photography, logos, icons, colors, typography, shape language, textures, product screenshots, and licensing constraints. Extend the local visual language before inventing one.
- Score each candidate slot by user value, relevance, visual leverage, implementation cost, accessibility risk, performance cost, and confidence. Record the exact route, component/landmark, surrounding content, intended viewport behavior, and why imagery is better than layout or copy alone.
- Define one style lock before individual prompts:
- asset family and medium;
- composition, perspective, depth, and geometry;
- palette and relationship to product tokens;
- lighting, contrast, texture, line/edge treatment, and detail level;
- character/object continuity and representation constraints;
- background treatment, whitespace, crop behavior, and motion potential;
- forbidden motifs and anti-goals.
- Make every prompt self-contained. Repeat the exact style-lock language, then add the asset's unique subject, action, composition, mood, focal point, background, crop-safe region, and exclusions. Do not rely on “same style as above” because generation calls may run independently.
- Describe visual attributes instead of requesting imitation of a living artist. Do not request copyrighted characters, fabricated logos, fake product screenshots, false testimonials, or readable interface text inside generated raster images. Use real product capture workflows for actual UI.
- Choose aspect ratio and resolution from the slot, not from a global default. A wide hero, square card, portrait story, and inline diagram may all need different dimensions while sharing the same style. For every asset specify: rendered size by breakpoint, source resolution or responsive renditions, focal point, safe crop, and whether separate mobile art direction is required.
- Specify accessibility and content behavior: informative alt text, empty alt for purely decorative assets, captions when needed, contrast under overlays, no essential meaning embedded only in the image, and reduced-motion handling for any later animation.
- Specify delivery constraints: preferred format, compression/quality target,
width and height attributes,
srcset/sizes where applicable, eager versus lazy loading, LCP priority, fallback, theme variants, and a realistic page-weight budget.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 106 lines · 21 tokens per session scan A a46f218d8a98
image-art-direction is a cursor rule published in the GitHub repository Oriolshhh/runware-image-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 1,207 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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